Sprecher
Beschreibung
Droplet motion on surfaces plays an important role in biological systems and technological applications [1–3]. Most natural and artificial superhydrophobic surfaces have irregular or stochastic roughness [4–6], making them challenging to model. However, all existing studies on wetting have been done for well-defined surfaces, such as micropillar arrays with a regular structure [7–11]. Here, I will show some of our recent fundings on how the behavior of droplets on stochastic surfaces fundamentally differs from that on regular surfaces. First, I will demonstrate how stochasticity influences droplet behavior in confined systems, enabling more viscous liquids to flow faster than those with lower viscosity [12]. Next, I will discuss the impact of stochastic roughness on droplet friction on flat surfaces. We found that the friction experienced by droplets at very low speeds (quasi-static regime, around micrometers per second) is ten times greater than at higher speeds (dynamic regime, from centimeters to meters per second) [13]. This finding contrasts sharply with current research [14,15]. Through imaging and modeling, we will examine how the adaptation of the droplet meniscus to surface roughness depends on droplet speed. Finally, I will introduce the concepts of static and dynamic solid fractions to explain these differences while highlighting the stochasticity levels required to repel droplets of varying sizes effectively. This knowledge paves the way for developing new designs for superhydrophobic surfaces.
References
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13. Vuckovac, M. et al., in preparation.
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15. Arunachalam, S., Lin, M. & Daniel, D. Probing the physical origins of droplet friction using a critically damped cantilever. Soft Matter 20, 7583–7591 (2024).